This paper analyses the use of factor analysis for instrumental variable estimation when the number of instruments tends to infinity. We consider cases where the unobserved factors are the optimal instruments but also cases where the factors are not necessarily the optimal instruments but can...

The estimation of dynamic factor models for large sets of variables has attracted considerable attention recently, due to the increased availability of large datasets. In this paper we propose a new methodology for estimating factors from large datasets based on state space models, discuss its...

This paper analyses the use of factor analysis for instrumental variable estimation when the number of instruments tends to infinity. In particular, we focus on situations where many weak instruments exist and/or the factor structure is weak. Theoretical results, simulation experiments and...

The estimation of dynamic factor models for large sets of variables has attracted considerable attention recently, due to the increased availability of large datasets. In this paper we propose a new methodology for estimating factors from large datasets based on state space models, discuss its...

This paper analyses the use of factor analysis for instrumental variable estimation when the number of instruments tends to infinity. We consider cases where the unobserved factors are the optimal instruments but also cases where the factors are not necessarily the optimal instruments but can...

The estimation of dynamic factor models for large sets of variables has attracted considerable attention recently, due to the increased availability of large datasets. In this paper we propose a new parametric methodology for estimating factors from large datasets based on state space models and...

The estimation of structural dynamic factor models (DFMs) for large sets of variables is attracting considerable attention. In this paper we briefly review the underlying theory and then compare the impulse response functions resulting from two alternative estimation methods for the DFM....

VARs are a popular tool for forecasting and structural analysis, but ill-suited to handle occasionally binding constraints, like the effective lower bound on nominal interest rates. We extend the VAR framework by modeling interest rates as censored observations of a latent shadow-rate process,...

We propose a new approach to forecasting the term structure of interest rates, which allows to efficiently extract the information contained in a large panel of yields. In particular, we use a large Bayesian Vector Autoregression (BVAR) with an optimal amount of shrinkage towards univariate AR...

We propose a new approach to forecasting the term structure of interest rates, which allows to efficiently extract the information contained in a large panel of yields. In particular, we use a large Bayesian Vector Autoregression (BVAR) with an optimal amount of shrinkage towards univariate AR...